Instructions to use sreeharivp23/fire-segmentation-yolo11n with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use sreeharivp23/fire-segmentation-yolo11n with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("sreeharivp23/fire-segmentation-yolo11n") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Fire Segmentation โ YOLO11n-seg
A YOLO11n instance-segmentation model fine-tuned to detect fire with pixel-accurate masks. Unlike bounding-box fire detectors, it reports the exact flame region, which makes it possible to measure how much of a frame is burning and to draw precise overlays for monitoring/alerting systems.
Usage
from ultralytics import YOLO
model = YOLO("fire_seg_yolo11n.pt")
# image, video file, folder, URL or webcam index all work
results = model.predict("fire_video.mp4", conf=0.35)
for r in results:
if r.masks is not None:
print(f"fire instances: {len(r.masks)}")
r.show() # or r.save()
Or from the CLI:
yolo segment predict model=fire_seg_yolo11n.pt source=fire_video.mp4 conf=0.35
Model details
| Base model | yolo11n-seg.pt (Ultralytics YOLO11 nano, segmentation) |
| Task | Instance segmentation, 1 class: fire |
| Input size | 640 ร 640 |
| Epochs | 60 |
| Mask mAP50 | 0.54 |
| Mask mAP50-95 | 0.29 |
| Box mAP50 | 0.55 |
Training data
The full auto-labelled dataset is published at sreeharivp23/fire-segmentation-dataset: ~1,100 fire images from the CAIR Fire-Detection-Image-Dataset and the DeepQuestAI Fire-Smoke-Dataset, plus 250 no-fire negatives.
Segmentation labels were machine-generated: a pretrained YOLOv8 fire/smoke bounding-box detector proposed fire regions, SAM 2.1 (base) converted each box into a pixel mask, and masks were polygonised into YOLO-seg format. An HSV flame-colour heuristic covered images the detector missed. Validation metrics above are measured against these auto-generated labels, not human annotations.
Limitations
- Labels are machine-generated; expect some mask noise, especially around smoke/glow boundaries and small or occluded flames.
- Trained mostly on visible orange/yellow flames; performance on blue flames, night-vision or thermal imagery is untested.
- Not a certified fire-safety system. Do not use as a substitute for smoke/fire alarms.
License
Fine-tuned from Ultralytics YOLO11, which is released under AGPL-3.0; these weights inherit that license.
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